論文

In this paper we develop a valuation model when there exist two competitive firms that face irreversible investment decisions under the demand uncertainty. We propose a numerical procedure to derive both project values and equilibrium strategies in the duopolistic environment. In numerical examples we consider two different economic conditions, which are labelled first mover advantage and second mover advantage, and examine the effects of these conditions on the equilibrium strategies as well as the project values. We show that these conditions cause significant changes in the equilibrium strategies of both firms.

This paper describes a simple and efficient method for determining the optimal portfolio for a risk averse investor. The portfolio selection problem is of long standing interest to finance scholars and it has obvious practical relevance. In a complete market the modern procedure for computing the optimal portfolio weights is known as the martingale approach. Recently, alternative implementations of the martingale approach based on Monte Carlo methods have been proposed. These methods use Monte Carlo simulation to compute stochastic integrals. This paper examines the efficient implementation of one of these methods due to [Cvitanic, J., Goukasian, L., Zapatero, F. 2003. Monte Carlo computation of optimal portfolios in complete markets. J. Econom. Dynam. Control 27, 971–986]. We explain why a naive application of the quasi-Monte Carlo method to this problem is often only marginally more efficient than the classical Monte Carlo method. Using the dimension reduction technique of [Imai, J., Tan, K.S., 2007. A general dimension reduction method for derivative pricing. J. Comput. Financ. 10 (2), 129–155] it is possible to significantly reduce the effective dimension of the problem. The paper shows why the proposed technique leads to a dramatic improvement in efficiency.

In this paper, we develop a simple and yet practically efficient algorithm for simulating high-dimensional exotic options. Our method is based on an extension of Imai and Tan's linear transformation method, which is originally proposed in the context of simulating a Gaussian process. By generalizing this method to other stochastic processes and exploiting the numerical inversion method of Hormann and Leydold, this method can be used to enhance quasi-Monte Carlo method in a wide range of applications. We demonstrate the relative efficiency of our proposed simulation technique using exotic option examples including Asian, lookback, barrier, and cliquet options for which the underlying asset price follows an exponential generalized hyperbolic Lévy process. We also illustrate the impact of our proposed method on dimension reduction. Read More: https://epubs.siam.org/doi/10.1137/080727713

This paper investigates the equilibrium investment policies of two different firms under consumers’ preference uncertainty. The incumbent firm, which owns a superior old technology, produces merchandise that can satisfy current consumers at the beginning of the investment game. The startup firm, which possesses an inferior old technology, does not capture the consumers satisfaction but it has a possibility to cultivate a new technology that can attract the consumers in the future if the customers’ preference is changed. We consider two types of equilibria in our valuation model. The first one is a price equilibrium at each time derived from the Bertrand competition. To represent customers’ diversity and products differentiation we use a discrete choice model. The other one is a Markov perfect equilibrium where each firm can invest either in the old technology or in the new technology depending on customers’ preference, which is modeled as a Markov process.

Infinitely divisible random vectors and Lévy processes without Gaussian component admit representations with shot noise series. To enhance efficiency of the series representation in Monte Carlo simulations, we discuss variance reduction methods, such as stratified sampling, control variates and importance sampling, applied to exponential interarrival times forming the shot noise series. We also investigate the applicability of the generalized linear transformation method in the quasi-Monte Carlo framework to random elements of the series representation. Although implementation of the proposed techniques requires a small amount of initial work, the techniques have the potential to yield substantial improvements in estimator efficiency, as the plain use of the series representation in those frameworks is often expensive. Numerical results are provided to illustrate the effectiveness of our approaches.

In this paper, we investigate simple yet practical schemes to generate random variates from the characteristic function of any continuous distribution. We discuss the generation of non-uniform random variates from a uniform random number generator. The inverse of the cumulative distribution function is derived from its characteristic function via the fast Fourier transform. We conduct several numerical experiments to assess the accuracy and efficiency of the schemes.